System and method for image conversion

ABSTRACT

A method may include obtaining a first set of projection data with respect to a first dose level; reconstructing, based on the first set of projection data, a first image; determining a second set of projection data based on the first set of projection data, the second set of projection data relating to a second dose level that is lower than the first dose level; reconstructing a second image based on the second set of projection data; and training a first neural network model based on the first image and the second image. In some embodiments, the trained first neural network model may be configured to convert a third image to a fourth image, the fourth image exhibiting a lower noise level and corresponding to a higher dose level than the third image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This present application is a continuation of International ApplicationNo. PCT/CN2017/095071, filed on Jul. 28, 2017, the contents of which arehereby incorporated by reference

TECHNICAL FIELD

The present disclosure generally relates to an imaging system, and morespecifically relates to methods and systems for converting a low-doseimage to a high-dose image.

BACKGROUND

Computed tomography (CT) is a technology that makes use ofcomputer-processed combinations of X-ray data taken from differentangles to produce 2D or 3D images. The CT technology has been widelyused in medical diagnosis. During a reconstruction process of a CT imagebased on low-dose projection data, noise and/or artifacts (e.g.,staircase artifacts) may appear in a reconstructed CT image. Theartifacts may reduce the image quality and influence the results ofdiagnosis on the basis of such an image. A high-dose CT scan may atleast partially alleviate these problems but at the cost of exposing ascanned object too more radiation. It is desirable to provide systemsand methods for generating a high-dose CT image of improved quality,based on a low-dose CT scan.

SUMMARY

According to an aspect of the present disclosure, a method forconverting a low-dose image to a high-dose image is provided. The methodmay be implemented on at least one machine each of which has at leastone processor and storage. The method may include obtaining a first setof projection data with respect to a first dose level; reconstructing,based on the first set of projection data, a first image; determining,based on the first set of projection data, a second set of projectiondata, the second set of projection data relating to a second dose levelthat is lower than the first dose level; reconstructing, based on thesecond set of projection data, a second image; and training a firstneural network model based on the first image and the second image, thetrained first neural network model being configured to convert a thirdimage to a fourth image, the fourth image exhibiting a lower noise leveland corresponding to a higher dose level than the third image.

In some embodiments, the first neural network model may be structuredbased on at least one of a Convolutional Neural Network (CNN), aRecurrent Neural Network (RNN), a Long Short-Term Memory (LSTM), or aGenerative Adversarial Network (GAN).

In some embodiments, the first image may be reconstructed based on aniterative reconstruction algorithm with first reconstruction parameters.

In some embodiments, the second image may be reconstructed based on ananalytical reconstruction algorithm, or an iterative reconstructionalgorithm with second reconstruction parameters. In some embodiments,the second reconstruction parameters may be, at least partially,different from the first parameters.

In some embodiments, the first image may be reconstructed by applying atleast one of a larger slice thickness, a larger reconstruction matrix,or a smaller FOV, compared to the reconstruction of the second image.

In some embodiments, the second set of projection data is determinedbased on at least one of a scanning parameter of a scanner that acquiresthe first projection data, an attenuation coefficient relating to asubject, and noises corresponding to the scanner, a response of a tube,a response of a detector of the scanner, a size of a focus of thescanner, a flying focus of the scanner, an integration time of thedetector of the scanner, or a scattering coefficient of the subject.

In some embodiments, the determining the second set of projection datamay include determining a first distribution of a radiation with respectto the second dose level before the radiation passing through a subject;determining a second distribution of the radiation after the radiationpassing through the subject based on the first distribution of theradiation and the first set of projection data; determining a noiseestimation of the scanner; and determining the second set of projectiondata, based on the second distribution of the radiation and the noiseestimation. In some embodiments, the determining the noise estimationmay include detecting a response of detectors in the scanner when noradiation is emitted from the scanner.

In some embodiments, the training the first neural network model basedon the first image and the second image may include extracting, from thefirst image, a first region; extracting, from the second image, a secondsub-region corresponding to the first region in the first image, thefirst region in the first image having a same size as the second region;and training the first neural network model based on the first region inthe first image and the second region in the second image.

In some embodiments, the training the first neural network model basedon the first region in the first image and the second region in thesecond image may include initializing parameter values of the firstneural network model; iteratively determining, at least based on thefirst region in the first image and the second region in the secondimage, a value of a cost function relating to the parameter values ofthe first neural network model in each iteration, including updating atleast some of the parameter values of the first neural network modelafter each iteration based on an updated value of the cost functionobtained in a most recent iteration; and determining the trained firstneural network model until a condition is satisfied.

In some embodiments, the condition may include that a variation of thevalues of the cost function among a plurality of iterations is below athreshold, or a threshold number of the iterations have been performed.

In some embodiments, the method may further include training a secondneural network model based on a sixth image and a seventh image. In someembodiments, the sixth image and the seventh image may be reconstructedbased on the third set of projection data. In some embodiments, an imagequality of the seventh image may be greater than that of the sixthimage. The image quality may relate to at least one of a contrast ratioand a spatial ratio.

In some embodiments, the third set of projection data may include thefirst set of projection data.

In some embodiments, a dimension of the first image or the first neuralnetwork model is no less than two.

According to another aspect of the present disclosure, a method forconverting a low-dose image to a high-dose image is provided. The methodmay be implemented on at least one machine each of which has at leastone processor and storage. The method may include obtaining a first setof projection data with respect to a first dose level; determining,based on a first neural network model and the first set of projectiondata, a second set of projection data with respect to a second doselevel that is higher than the first dose level; generating, based on thesecond set of projection data, a first image; generating, based on asecond neural network model and the first image, a second image.

In some embodiments, the first neural network model may be generated byobtaining a third set of projection data with respect to a third doselevel; simulating, based on the third set of projection data, a fourthset of projection data, the fourth set of projection data relating to afourth dose level that is lower than the third dose level; and trainingthe first neural network model based on the third set of projection dataand the fourth set of projection data.

In some embodiments, the simulating the fourth set of projection datamay include determining a first distribution of a radiation with respectto the fourth dose level before the radiation passing through a subject;determining, based on the first distribution of the radiation and thethird set of projection data, a second distribution of the radiationafter the radiation passing through the subject; determining a noiseestimation of a scanner; an determining, based on the seconddistribution of the radiation and the noise estimation, the fourth setof projection data.

In some embodiments, the second neural network may be generated byobtaining a third image, the third image being reconstructed based on afifth set of projection data, and obtaining a fourth image, the fourthimage being reconstructed based on the fifth set of projection data;training the second neural network model based on the third image andthe fourth image. In some embodiments, an image quality of the fourthimage may be greater than that of the third image, the image qualityrelating to at least one of a contrast ratio and a spatial resolution.

In some embodiments, the fifth set of projection data may include thefirst set of projection data.

In some embodiments, a dimension of the first image or the first neuralnetwork model may be no less than two.

In some embodiments, the first dose level may be 5 mSv or above.

In some embodiments, the first dose level may be 15 mSv or above.

In some embodiments, the second dose level may be 10% or below of thefirst dose level.

In some embodiments, the second dose level may be 40% or below of thefirst dose level.

According to an aspect of the present disclosure, a system forconverting a low-dose image to a high-dose image is provided. The systemmay include at least one processor and executable instructions. When theexecutable instructions are executed by the at least one processor, theinstructions may cause the at least one processor to implement a method.The method may include obtaining a first set of projection data withrespect to a first dose level; reconstructing, based on the first set ofprojection data, a first image; determining, based on the first set ofprojection data, a second set of projection data, the second set ofprojection data relating to a second dose level that is lower than thefirst dose level; reconstructing, based on the second set of projectiondata, a second image; and training a first neural network model based onthe first image and the second image, the trained first neural networkmodel being configured to convert a third image to a fourth image, thefourth image exhibiting a lower noise level and corresponding to ahigher dose level than the third image.

According to another aspect of the present disclosure, a non-transitorycomputer readable medium is provided. The non-transitory computerreadable medium may include executable instructions. When theinstructions are executed by at least one processor, the instructionsmay cause the at least one processor to implement a method. The methodmay include obtaining a first set of projection data with respect to afirst dose level; reconstructing, based on the first set of projectiondata, a first image; determining, based on the first set of projectiondata, a second set of projection data, the second set of projection datarelating to a second dose level that is lower than the first dose level;reconstructing, based on the second set of projection data, a secondimage; and training a first neural network model based on the firstimage and the second image, the trained first neural network model beingconfigured to convert a third image to a fourth image, the fourth imageexhibiting a lower noise level and corresponding to a higher dose levelthan the third image.

According to an aspect of the present disclosure, a system forconverting a low-dose image to a high-dose image is provided. The systemmay include an image data simulation unit. The image data simulationunit may be configured to determine, based on a first set of projectiondata, a second set of projection data, wherein the first set ofprojection data may relate to a first dose level, and the second set ofprojection data may relate to a second dose level that is lower than thefirst dose level. The system may further include an image reconstructionunit that is configured to reconstruct a first image based on the firstset of projection data and reconstruct a second image based on thesecond set of projection data. The system may further include a neuralnetwork training unit that is configured to train a first neural networkmodel based on the first image and the second image, the trained firstneural network model being configured to convert a third image to afourth image, the fourth image exhibiting a lower noise level andcorresponding to a higher dose level than the third image.

According to an aspect of the present disclosure, a system forconverting a low-dose image to a high-dose image is provided. The systemmay include at least one processor and executable instructions. When theexecutable instructions are executed by the at least one processor, theinstructions may cause the at least one processor to implement a method.The method may include obtaining a first set of projection data withrespect to a first dose level; determining, based on a first neuralnetwork model and the first set of projection data, a second set ofprojection data with respect to a second dose level that is higher thanthe first dose level; generating, based on the second set of projectiondata, a first image; generating, based on a second neural network modeland the first image, a second image.

According to another aspect of the present disclosure, a non-transitorycomputer readable medium is provided. The non-transitory computerreadable medium may include executable instructions. When theinstructions are executed by at least one processor, the instructionsmay cause the at least one processor to implement a method. The methodmay include obtaining a first set of projection data with respect to afirst dose level; determining, based on a first neural network model andthe first set of projection data, a second set of projection data withrespect to a second dose level that is higher than the first dose level;generating, based on the second set of projection data, a first image;generating, based on a second neural network model and the first image,a second image.

According to an aspect of the present disclosure, a system forconverting a low-dose image to a high-dose image is provided. The systemmay include an acquisition module. The acquisition module may beconfigured to obtain a first set of projection data with respect to afirst dose level. The system may further include an image dataprocessing module. The image data processing module may be configured todetermine, based on a first neural network model and the first set ofprojection data, a second set of projection data with respect to asecond dose level that is higher than the first dose level; generate,based on the second set of projection data, a first image; and generate,based on a second neural network model and the first image, a secondimage.

According to another aspect of the present disclosure, a method fortraining a neural network is provided. The method may be implemented onat least one machine each of which has at least one processor andstorage. The method may include obtaining a first set of projection datawith respect to a first dose level; determining, based on the first setof projection data, a second set of projection data, the second set ofprojection data relating to a second dose level that is lower than thefirst dose level; and training a neural network model based on the firstset of projection data and the second set of projection data, thetrained neural network model being configured to convert a third set ofprojection data to a fourth set of projection data, the fourth set ofprojection data having a lower noise level than the third set ofprojection data.

According to another aspect of the present disclosure, a method fortraining a neural network is provided. The method may be implemented onat least one machine each of which has at least one processor andstorage. The method may include obtaining projection data with respectto a dose level; reconstructing, based on the projection data, a firstimage by a first reconstruction parameter; reconstructing, based on theprojection data, a second image by a second reconstruction parameter,the second reconstruction parameter being different from the firstreconstruction parameter; and training a neural network model based onthe first image and the second image, the neural network model beingconfigured to convert a third image to a fourth image, wherein thefourth image exhibits greater image quality than the third image, theimage quality relating to at least one of a contrast ratio and a spatialresolution.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary CT imagingsystem according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device according to someembodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device according to someembodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing engineaccording to some embodiments of the present disclosure;

FIG. 5 is a block diagram illustrating an exemplary neural networkdetermination module according to some embodiments of the presentdisclosure;

FIG. 6 is a flowchart illustrating an exemplary process for processingimage data according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for determininga first neural network model according to some embodiments of thepresent disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for simulatinglow-dose projection data according to some embodiments of the presentdisclosure;

FIG. 9 is a flowchart illustrating an exemplary process for determininga second neural network model according to some embodiments of thepresent disclosure;

FIG. 10 is a flowchart illustrating an exemplary process 1000 fortraining a neural network model according to some embodiments of thepresent disclosure;

FIG. 11 is a schematic diagram illustrating an exemplary neural networkmodel according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by otherexpression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in a firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and components for image processing. In someembodiments, the imaging system may include a single-modality imagingsystem, such as a computed tomography (CT) system, an emission computedtomography (ECT) system, an ultrasonography system, an X-ray photographysystem, a positron emission tomography (PET) system, or the like, or anycombination thereof. In some embodiments, the imaging systrem may incudea multi-modality imaging system, such as, a computed tomography-magneticresonance imaging (CT-MRI) system, a positron emissiontomography-magnetic resonance imaging (PET-MRI) system, a single photonemission computed tomography-computed tomography (SPECT-CT) system, adigital subtraction angiography-computed tomography (DSA-CT) system,etc. It should be noted that the CT imaging system 100 described belowis merely provided for illustration purposes, and not intended to limitthe scope of the present disclosure.

For illustration purposes, the disclosure describes systems and methodsfor CT image processing. The system may generate a CT image based on aneural network model. For example, low-dose CT image data may beprocessed based on a neural network model to generate high-dose CT imagedata. The high-dose CT image data may exhibit a higher quality than thelow-dose CT image data. The neural network model may be obtained from atraining based on multiple low-dose images or image data, as well ashigh-doses images reconstructed based on different reconstructiontechniques, respectively.

The following description is provided to help better understanding CTimage reconstruction methods and/or systems. This is not intended tolimit the scope the present disclosure. For persons having ordinaryskills in the art, a certain amount of variations, changes, and/ormodifications may be deducted under the guidance of the presentdisclosure. Those variations, changes, and/or modifications do notdepart from the scope of the present disclosure.

FIG. 1 is schematic diagrams illustrating an exemplary CT imaging system100 according to some embodiments of the present disclosure. As shown,the CT imaging system 100 may include a scanner 110, a processing engine120, a storage 130, one or more terminals 140, and a network 150. Insome embodiments, the scanner 110, the processing engine 120, thestorage 130, and/or the terminal(s) 140 may be connected to and/orcommunicate with each other via a wireless connection (e.g., the network150), a wired connection, or a combination thereof. The connectionbetween the components in the CT imaging system 100 may be variable.Merely by way of example, the scanner 110 may be connected to theprocessing engine 120 through the network 150, as illustrated in FIG. 1.As another example, the scanner 110 may be connected to the processingengine 120 directly. As a further example, the storage 130 may beconnected to the processing engine 120 through the network 150, asillustrated in FIG. 1, or connected to the processing engine 120directly. As still a further example, a terminal 140 may be connected tothe processing engine 120 through the network 150, as illustrated inFIG. 1, or connected to the processing engine 120 directly.

The scanner 110 may generate or provide image data via scanning asubject, or a part of the subject. In some embodiments, the scanner 110may include a single-modality scanner and/or multi-modality scanner. Thesingle-modality may include, for example, a computed tomography (CT)scanner, a positron emission tomography (PET) scanner, etc. Themulti-modality scanner may include a single photon emission computedtomography-computed tomography (SPECT-CT) scanner, a positron emissiontomography-computed tomography (CT-PET) scanner, a computedtomography-ultra-sonic (CT-US) scanner, a digital subtractionangiography-computed tomography (DSA-CT) scanner, or the like, or acombination thereof. In some embodiments, the image data may includeprojection data, images relating to the subject, etc. The projectiondata may be raw data generated by the scanner 110 by scanning thesubject, or data generated by a forward projection on an image relatingto the subject. In some embodiments, the subject may include a body, asubstance, an object, or the like, or a combination thereof. In someembodiments, the subject may include a specific portion of a body, suchas a head, a thorax, an abdomen, or the like, or a combination thereof.In some embodiments, the subject may include a specific organ or regionof interest, such as an esophagus, a trachea, a bronchus, a stomach, agallbladder, a small intestine, a colon, a bladder, a ureter, a uterus,a fallopian tube, etc.

In some embodiments, the scanner 110 may include a tube, a detector,etc. The tube may generate and/or emit one or more radiation beamstravelling toward the subject according to one or more scanningparameters. The radiation may include a particle ray, a photon ray, orthe like, or a combination thereof. In some embodiments, the radiationmay include a plurality of radiation particles (e.g., neutrons, protons,electron, p-mesons, heavy ions, etc.), a plurality of radiation photons(e.g., X-ray, a y-ray, ultraviolet, laser, etc.), or the like, or acombination thereof. Exemplary scanning parameters may include a tubecurrent/voltage, an integration time of a detector, a focus size of atube, a response of a detector, a response of a tube, a width of acollimation, a slice thickness, a slice gap, a field of view (FOV), etc.In some embodiments, the scanning parameters may relate to a dose levelof the radiation emitted from the tube. As used herein, the dose levelof the radiation may be defined by a CT dose index (CTDI), an effectivedose, a dose-length product, etc. The CT dose index (CTDI) may refer tothe radiation energy of radiation corresponding to a single slice alonga long axis (e.g., the axial direction) of the scanner 110. Thedose-length product may refer to the total radiation energy of radiationreceived by a subject being examined in an integrated scanningprocedure. The effective dose may refer to radiation energy of radiationreceived by a specific region of a subject in an integrated scanningprocedure.

The detector in the scanner 110 may detect one or more radiation beamsemitted from the tube. In some embodiments, the detector of the scanner110 may include one or more detector units that may detect adistribution of the radiation beams emitted from the tube. In someembodiments, the detector of the scanner 110 may be connected to a dataconversation circuit configured to convert the distribution of thedetected radiation beams into image data (e.g., projection data). Theimage data may correspond to the dose level of a detected radiationbeams. In some embodiments, the dose level of the detected radiationbeams may include noise represented in the image data. For example, thehigher the dose level of radiation is, the lower the noise levelrelative to true signal (reflecting actual anatomy) represented in theimage data may be. The lower the dose-level of radiation is, the higherthe noise level represented in the image data may be.

The processing engine 120 may process data and/or information obtainedfrom the scanner 110, the storage 130, and/or the terminal(s) 140. Forexample, the processing engine 120 may reconstruct an image based onprojection data generated by the scanner 110. As another example, theprocessing engine 120 may determine one or more neural network modelsconfigured to process and/or convert an image. In some embodiments, theprocessing engine 120 may be a single server or a server group. Theserver group may be centralized or distributed. In some embodiments, theprocessing engine 120 may be local or remote. For example, theprocessing engine 120 may access information and/or data from thescanner 110, the storage 130, and/or the terminal(s) 140 via the network150. As another example, the processing engine 120 may be directlyconnected to the scanner 110, the terminal(s) 140, and/or the storage130 to access information and/or data. In some embodiments, theprocessing engine 120 may be implemented on a cloud platform. Forexample, the cloud platform may include a private cloud, a public cloud,a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud,a multi-cloud, or the like, or a combination thereof. In someembodiments, the processing engine 120 may be implemented by a computingdevice 200 having one or more components as described in connection withFIG. 2.

The storage 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage 130 may store dataobtained from the processing engine 120, the terminal(s) 140, and/or theinteraction device 150. In some embodiments, the storage 130 may storedata and/or instructions that the processing engine 120 may execute oruse to perform exemplary methods described in the present disclosure. Insome embodiments, the storage 130 may include a mass storage, aremovable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagemay include a magnetic disk, an optical disk, a solid-state drive, etc.Exemplary removable storage may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (EPROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage 130 may be implemented on a cloudplatform as described elsewhere in the disclosure.

In some embodiments, the storage 130 may be connected to the network 150to communicate with one or more other components in the CT imagingsystem 100 (e.g., the processing engine 120, the terminal(s) 140, etc.).One or more components in the CT imaging system 100 may access the dataor instructions stored in the storage 130 via the network 150. In someembodiments, the storage 130 may be part of the processing engine 120.

The terminal(s) 140 may be connected to and/or communicate with thescanner 110, the processing engine 120, and/or the storage 130. Forexample, the terminal(s) 140 may obtain a processed image from theprocessing engine 120. As another example, the terminal(s) 140 mayobtain image data acquired via the scanner 110 and transmit the imagedata to the processing engine 120 to be processed. In some embodiments,the terminal(s) 140 may include a mobile device 140-1, a tablet computer140-2, a laptop computer 140-3, or the like, or any combination thereof.For example, the mobile device 140-1 may include a mobile phone, apersonal digital assistance (PDA), a gaming device, a navigation device,a point of sale (POS) device, a laptop, a tablet computer, a desktop, orthe like, or any combination thereof. In some embodiments, theterminal(s) 140 may include an input device, an output device, etc. Theinput device may include alphanumeric and other keys that may be inputvia a keyboard, a touch screen (for example, with haptics or tactilefeedback), a speech input, an eye tracking input, a brain monitoringsystem, or any other comparable input mechanism. The input informationreceived through the input device may be transmitted to the processingengine 120 via, for example, a bus, for further processing. Other typesof the input device may include a cursor control device, such as amouse, a trackball, or cursor direction keys, etc. The output device mayinclude a display, a speaker, a printer, or the like, or a combinationthereof. In some embodiments, the terminal(s) 140 may be part of theprocessing engine 120.

The network 150 may include any suitable network that can facilitateexchange of information and/or data for the CT imaging system 100. Insome embodiments, one or more components of the CT imaging system 100(e.g., the scanner 110, the processing engine 120, the storage 130, theterminal(s) 140, etc.) may communicate information and/or data with oneor more other components of the CT imaging system 100 via the network150. For example, the processing engine 120 may obtain image data fromthe scanner 110 via the network 150. As another example, the processingengine 120 may obtain user instruction(s) from the terminal(s) 140 viathe network 150. The network 150 may be and/or include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN), a wide area network (WAN)), etc.), a wired network (e.g., anEthernet network), a wireless network (e.g., an 802.11 network, a Wi-Finetwork, etc.), a cellular network (e.g., a Long Term Evolution (LTE)network), a frame relay network, a virtual private network (VPN), asatellite network, a telephone network, routers, hubs, switches, servercomputers, and/or any combination thereof. For example, the network 150may include a cable network, a wireline network, a fiber-optic network,a telecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 150 may include one or more network accesspoints. For example, the network 150 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the CT imaging system 100may be connected to the network 150 to exchange data and/or information.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, thestorage 130 may be a data storage including cloud computing platforms,such as, public cloud, private cloud, community, and hybrid clouds, etc.However, those variations and modifications do not depart the scope ofthe present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device 200 on which theprocessing engine 120 may be implemented according to some embodimentsof the present disclosure. As illustrated in FIG. 2-A, the computingdevice 200 may include a processor 210, a storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing engine 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process image dataobtained from the CT scanner 110, the terminals 140, the storage 130,and/or any other component of the CT imaging system 100. In someembodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method operations that are performedby one processor as described in the present disclosure may also bejointly or separately performed by the multiple processors. For example,if in the present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperation s A and B).

The storage 220 may store data/information obtained from the CT scanner110, the terminals 140, the storage 130, and/or any other component ofthe CT imaging system 100. In some embodiments, the storage 220 mayinclude a mass storage, a removable storage, a volatile read-and-writememory, a read-only memory (ROM), or the like, or any combinationthereof. For example, the mass storage may include a magnetic disk, anoptical disk, a solid-state drives, etc. The removable storage mayinclude a flash drive, a floppy disk, an optical disk, a memory card, azip disk, a magnetic tape, etc. The volatile read-and-write memory mayinclude a random access memory (RAM). The RAM may include a dynamic RAM(DRAM), a double date rate synchronous dynamic RAM (DDR SDRAM), a staticRAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM),etc. The ROM may include a mask ROM (MROM), a programmable ROM (PROM),an erasable programmable ROM (EPROM), an electrically erasableprogrammable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digitalversatile disk ROM, etc. In some embodiments, the storage 220 may storeone or more programs and/or instructions to perform exemplary methodsdescribed in the present disclosure. For example, the storage 220 maystore a program for the processing engine 120 for determining aregularization item.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing engine 120. In some embodiments, the I/O 230 may include aninput device and an output device. Examples of the input device mayinclude a keyboard, a mouse, a touch screen, a microphone, or the like,or a combination thereof. Examples of the output device may include adisplay device, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Examples of the display device may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), a touch screen, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing engine 120 and theCT scanner 110, the terminals 140, and/or the storage 130. Theconnection may be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee link, a mobile networklink (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof. Insome embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 on which theterminals 140 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 3, the mobile device 300 mayinclude a communication platform 310, a display 320, a graphicprocessing unit (GPU) 330, a central processing unit (CPU) 340, an I/O350, a memory 360, and a storage 390. In some embodiments, any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 300.In some embodiments, a mobile operating system 370 (e.g., iOS™,Android™, Windows Phone™, etc.) and one or more applications 380 may beloaded into the memory 360 from the storage 390 in order to be executedby the CPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information relating toimage processing or other information from the processing engine 120.User interactions with the information stream may be achieved via theI/O 350 and provided to the processing engine 120 and/or othercomponents of the CT imaging system 100 via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram illustrating an exemplary processing engine120 according to some embodiments of the present disclosure. Theprocessing engine 120 may include an acquisition module 410, a controlmodule 420, a neural network determination module 430, an image dataprocessing module 440, and a storage module 450. The processing engine120 may be implemented on various components (e.g., the processor 210 ofthe computing device 200 as illustrated in FIG. 2). For example, atleast a portion of the processing engine 120 may be implemented on acomputing device as illustrated in FIG. 2 or a mobile device asillustrated in FIG. 3.

The acquisition module 410 may acquire image data. The acquisitionmodule 410 may acquire the image data from the scanner 110, or a storagedevice (e.g., the storage 130, the storage 220, the storage 390, thememory 360, the storage module 450, or the like, or a combinationthereof). The image data may include projection data, images, etc. Insome embodiments, the acquisition module 410 may transmit the acquiredimage data to other modules or units of the processing engine 120 forfurther processing. For example, the acquired image data may betransmitted to the storage module 450 for storage. As another example,the acquisition module 410 may transmit the image data (e.g., projectiondata) to the image data processing module 440 to reconstruct an image.

The control module 420 may control operations of the acquisition module410, the neural network determination module 430, the image processingmodule 440, and/or the storage module 450 (e.g., by generating one ormore control parameters). For example, the control module 420 maycontrol the acquisition module 410 to acquire image data. As anotherexample, the control module 420 may control the image data processingmodule 440 to process the image data acquired by the acquisition module410. As still another example, the control module 420 may control theneural network determination module 430 to train a neural network model.In some embodiments, the control module 420 may receive a real-timecommand or retrieve a predetermined command provided by, e.g., a user(e.g., a doctor) or the system 100 to control one or more operations ofthe acquisition module 410, the neural network determination module 430,and/or the image data processing module 440. For example, the controlmodule 420 can adjust the image data processing module 440 to generateimages of a subject according to the real-time command and/or thepredetermined command. In some embodiments, the control module 420 maycommunicate with one or more other modules of the processing engine 120for exchanging information and/or data.

The neural network determination module 430 may determine one or moreneural network models. For example, the neural network determinationmodule 430 may determine a first neural network model configured to, forexample, reduce the noise level in an image. As another example, theneural network determination module 430 may determine a second neuralnetwork model configured to, for example, increase a contrast ratio ofan image, by performing, for example, an image enhancement operation onthe image. In some embodiments, the neural network determination module430 may transmit a determined neural network model to one or more othermodules for further processing or application. For example, the neuralnetwork determination module 430 may transmit a neural network model tothe storage module 450 for storage. As another example, the neuralnetwork determination module 430 may transmit a neural network model tothe image data processing module 440 for image processing.

The image data processing module 440 may process information provided byvarious modules of the processing engine 120. The processing module 440may process image data acquired by the acquisition module 410, imagedata retrieved from the storage module 450, etc. In some embodiments,the image data processing module 440 may reconstruct an image based onthe image data according to a reconstruction technique, generate areport including one or more images and/or other related information,and/or perform any other function for image reconstruction in accordancewith various embodiments of the present disclosure.

The storage module 450 may store image data, models, control parameters,processed image data, or the like, or a combination thereof. In someembodiments, the storage module 450 may store one or more programsand/or instructions that may be executed by the processor(s) of theprocessing engine 120 to perform exemplary methods described in thisdisclosure. For example, the storage module 450 may store program(s)and/or instruction(s) that can be executed by the processor(s) of theprocessing engine 120 to acquire image data, reconstruct an image basedon the image data, train a neural network model, and/or display anyintermediate result or a resultant image.

In some embodiments, one or more modules illustrated in FIG. 4 may beimplemented in at least part of the exemplary CT imaging system 100 asillustrated in FIG. 1. For example, the acquisition module 410, thecontrol module 420, the storage module 450, the neural networkdetermination module 430, and/or the image data processing module 440may be integrated into a console (not shown). Via the console, a usermay set parameters for scanning a subject, controlling imagingprocesses, controlling parameters for reconstruction of an image,viewing reconstructed images, etc. In some embodiments, the console maybe implemented via the processing engine 120 and/or the terminals 140.In some embodiments, the neural network determination 430 may beintegrated into the terminals 140.

In some embodiments, the processing engine 120 does not include theneural network determination module 430. One or more neural networkmodels determined by another device may be stored in the system 100(e.g., the storage 130, the storage 220, the storage 390, the memory360, the storage module 450, etc.) or in an external device accessibleby the processing engine 120 via, for example, the network 150. In someembodiments, such a device may include a portion the same as or similarto the neural network determination module 430. In some embodiments, theneural network determination module 430 may store one or more neuralnetwork models determined by another device and be accessible by one ormore components of the system 100 (e.g., the image reconstruction unit520, the image data simulation unit 540, etc.). In some embodiments, aneural network model applicable in the present disclosure may bedetermined by the system 100 (or a portion thereof including, e.g., theprocessing engine 120) or an external device accessible by the system100 (or a portion thereof including, e.g., the processing engine 120)following the processes disclosure herein. See, for example, FIGS. 7, 9and 10, and the description thereof.

FIG. 5 is a block diagram illustrating an exemplary neural networkdetermination module 430 according to some embodiments of the presentdisclosure. As shown, the neural network determination module 430 mayinclude an image reconstruction unit 520, an image data simulation unit540, a neural network training unit 560, and a storage unit 580. Theneural network determination module 430 may be implemented on variouscomponents (e.g., the processor 210 of the computing device 200 asillustrated in FIG. 2). For example, at least a portion of the neuralnetwork determination module 430 may be implemented on a computingdevice as illustrated in FIG. 2 or a mobile device as illustrated inFIG. 3.

The image reconstruction unit 520 may reconstruct one or more imagesbased on one or more reconstruction techniques. In some embodiments, theimage reconstruction unit 520 may reconstruct a first image (e.g., ahigh-dose image) based on a first reconstruction technique. The imagereconstruction unit 520 may reconstruct a second image (e.g., a low-doseimage) based on a second reconstruction technique. The firstreconstruction technique and the second reconstruction technique may bethe same or different. In some embodiments, the image reconstructionunit 520 may transmit the reconstructed images to other units or blocksof the neural network determination module 430 for further processing.For example, the image reconstruction unit 520 may transmit thereconstructed images to the neural network training unit 560 fortraining a neural network model. As another example, the imagereconstruction unit 520 may transmit the reconstructed images to thestorage unit 580 for storage.

The image data simulation unit 540 may simulate image data. In someembodiments, the image data simulation unit 540 may simulate virtuallow-dose image data based on high-dose image data acquired by way of aCT scan. As used herein, the virtual low-dose image data may correspondto a lower dose level than that of the true high-dose image data. Insome embodiments, the image data simulation unit 540 may transmit thesimulated image data to other units and/or blocks in the neural networkdetermination module 430 for further processing. For example, thesimulated image data may be transmitted to the image reconstruction unit520 for generating an image. As another example, the simulated imagedata may be transmitted to the neural network training unit 560 fortraining a neural network model.

The neural network training unit 560 may train a neural network model.In some embodiments, the neural network training unit 560 may train afirst neural network model configured to, for example, reduce the noiselevel in an image. Such a neural network model may be obtained usingmultiple high-dose images and corresponding low-dose images. In someembodiments, the neural network training unit 560 may train a secondneural network model configured to, for example, improve a contrastratio of an image. Such a neural network model may be obtained usingmultiple images with higher contrast ratios and corresponding imageswith lower contrast ratios. As used herein, two images may be consideredcorresponding to each other when both images relate to a same region ofa subject. Merely by way of example, two corresponding images may bedifferent in one or more aspects including, for example, a high-doseimage vs. a low-dose image, an image having a high contrast ratio vs. animage having a low contrast ratio, or the like, or a combinationthereof.

In some embodiments, the neural network training unit 560 may furtherinclude an initialization block 562, an extraction block 564, acalculation block 566, and a judgment block 568. The initializationblock 562 may initialize a neural network model. For example, theinitialization block 562 may construct an initial neural network model.As another example, the initialization block 562 may initialize one ormore parameter values of the initial neural network model. Theextraction block 564 may extract information from one or more trainingimages (e.g., high-dose images and low-dose images). For example, theextraction block 564 may extract features regarding one or more regionsfrom the training images. The calculation block 566 may perform acalculation function in a process for, for example, training a neuralnetwork model. For example, the calculation block 566 may calculate oneor more parameter values of an updated neural network model generated inan iterative training process. The judgment block 568 may perform ajudgment function in a process for, for example, training a neuralnetwork model. For example, the judgment block 568 may determine whethera condition satisfies in a training process of a neural network model.

The storage unit 580 may store information relating to, for example,training a neural network model. In some embodiments, the informationrelating to training a neural network model may include images fortraining a neural network model, algorithms for training a neuralnetwork model, parameters of a neural network model, etc. For example,the storage unit 580 may store training images (e.g., high-dose imagesand low-dose images) according to a certain criterion. The trainingimages may be stored or uploaded into the storage unit 580 based ondimensions of the training images. For illustration purposes, atwo-dimensional (2D) image or a three-dimensional (3D) image may bestored as a 2D or 3D matrix including a plurality of elements (e.g.,pixels or voxels). The elements of the 2D matrix may be arranged in thestorage unit 580 in a manner that each row of elements, corresponding tothe length of the 2D image, are orderly stored in the storage unit 580,and thus the elements in a same row may be adjacent to each other in thestorage unit 580. The elements of the 3D matrix may be arranged in thestorage unit 580 in a manner that multiple 2D matrixes that compose the3D matrix may be orderly stored in the storage unit 580, and then therows and/or the columns of each 2D matrix may be orderly stored in thestorage unit 580. The storage unit 580 may be a memory that stores datato be processed by processing devices, such as CPUs, GPUs, etc. In someembodiments, the storage unit 580 may be a memory that may be accessibleby one or more GPUs, or may be memory that is only accessible by aspecific GPU.

It should be noted that the above description of the processing module430 is merely provided for the purposes of illustration, and notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations or modificationsmay be made under the teachings of the present disclosure. However,those variations and modifications do not depart from the scope of thepresent disclosure. For example, the image reconstruction unit 520 andthe image data simulation unit 540 may be integrated into one singleunit.

FIG. 6 is a flowchart illustrating an exemplary process 600 forprocessing image data according to some embodiments of the presentdisclosure. In some embodiments, one or more operations of process 600illustrated in FIG. 6 for processing image data may be implemented inthe CT imaging system 100 illustrated in FIG. 1. For example, theprocess 600 illustrated in FIG. 6 may be stored in the storage 130 inthe form of instructions, and invoked and/or executed by the processingengine 120 (e.g., the processor 210 of the computing device 200 asillustrated in FIG. 2, the GPU 330 or CPU 340 of the mobile device 300as illustrated in FIG. 3).

In 602, low-dose image data may be obtained. Operation 602 may beperformed by the acquisition module 410. As used herein, the low-doseimage data may refer to the image data (e.g., projection data, an image,etc.) corresponding to a first dose level. In some embodiments, thelow-dose image data may include low-dose projection data. In someembodiments, the low-dose image data may include a low-dose image. Insome embodiments, the low-dose image data may include two-dimensional(2D) image data, three-dimensional (3D) image data, four-dimensional(4D) image data, or image data of other dimensions. In some embodiments,the low-dose image data may be true image data obtained from a scanner(e.g., the scanner 110) generated by scanning a subject at a low-doselevel (e.g., the first dose level). In some embodiments, the low-doseimage data may be virtual image data that is obtained by way ofsimulation from other image data, e.g., high-dose image data. In someembodiments, the low-dose image data may be obtained from the storage130, the terminal(s) 140, the storage module 450, and/or any otherexternal storage device.

In 604, a first neural network model may be acquired. Operation 604 maybe performed by the neural network determination module 430. In someembodiments, the first neural network model may be pre-determined (e.g.,provided by a manufacturer of the CT scanner, an entity specializing inimage processing, an entity having access to training data, etc.) Insome embodiments, the first neural network model may be configured toprocess image data (e.g., the low-dose image data obtained in 602).Exemplary image data processing may include transform, modification,and/or conversion, etc. For example, the first neural network model maybe configured to convert the low-dose image data to high-dose image datacorresponding to the low-dose image data. As another example, the firstneural network model may be configured to reduce the noise level inimage data (e.g., the low-dose image data obtained in 602). In someembodiments, the first neural network model may be constructed based ona convolutional neural network (CNN), a recurrent neural network (RNN),a long short-term memory (LSTM), a generative adversarial network (GAN),or the like, or a combination thereof. See, for example, FIG. 11 and thedescription thereof. In some embodiments, the first neural network modelmay be constructed as a two-dimensional (2D) model, a three-dimensional(3D) model, a four-dimensional (4D) model, or a model of any otherdimensions. In some embodiments, a first neural network model may bedetermined according to process 700 as illustrated in FIG. 7.

In 606, the low-dose image data may be processed based on the firstneural network model to generate (virtual) high-dose image datacorresponding to the low-dose image data. Operation 606 may be performedby the image data processing module 440. In some embodiments, the(virtual) high-dose image data corresponding the low-dose image data mayexhibit a lower noise level than that of the low-dose image data. Asused herein, the (virtual) high-dose image data corresponding to thelow-dose image data may refer to the image data (e.g., projection data,an image, etc.) corresponding to a second dose level. The second doselevel of the (virtual) high-dose image data may be greater than thefirst dose level of the low-dose image data. The corresponding (virtual)high-dose image data and low-dose image data may refer to therepresentation of a same subject or a same portion or region of thesubject being examined (e.g., a patient, a tissue, etc.). In someembodiments, the (virtual) high-dose image data may include high-doseprojection data. In some embodiments, the (virtual) high-dose image datamay include a high-dose image. In some embodiments, the high-dose imagedata may include 2D image data, 3D image data, 4D image data, or imagedata of another dimension.

In 608, a second neural network model may be acquired. Operation 608 maybe performed by the neural network model determination module 430. Insome embodiments, the second neural network model may be pre-determined(e.g., provided by a manufacturer of the CT scanner, an entityspecializing in image processing, an entity having access to trainingdata, etc.) In some embodiments, the second neural network model may beconfigured to process image data (e.g., the (virtual) high-dose imagedata generated in 606). Exemplary image data processing may includetransform, modification, and/or conversion, etc. For example, the secondneural network model may be configured to perform an image dataenhancement operation on the image data (e.g., the (virtual) high-doseimage data generated in 606). In some embodiments, the second neuralnetwork model may be constructed based on a convolutional neural network(CNN), a recurrent neural network (RNN), a long short-term memory(LSTM), a generative adversarial network (GAN), or the like, or acombination thereof. See, for example, FIG. 11 and the descriptionthereof. In some embodiments, the second neural network model may beconstructed as a two-dimensional (2D) model, a three-dimensional (3D)model, a four-dimensional (4D) model, or a model of any otherdimensions. In some embodiments, a second neural network model may bedetermined according to process 900 as illustrated in FIG. 9.

In 610, the (virtual) high-dose image data may be post-processed basedon the second neural network model. Operation 610 may be performed bythe image data processing module 440. In some embodiments, thepost-processed high-dose image data may exhibit a higher quality thanthat of the high-dose image data acquired at 608. For example, thepost-processed high-dose image data corresponding the high-dose imagedata may exhibit a higher contrast ratio than that of the high-doseimage data acquired at 608.

In 612, the post-processed high-dose image data may be outputted.Operation 612 may be performed by image data processing module 440. Insome embodiments, the post-processed high-dose image data may beoutputted to the terminal(s) 140 for display in the form of, e.g., animage. In some embodiments, the post-processed high-dose image data maybe outputted to the storage device 130 and/or the storage module 508 forstorage.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,process 600 may include a pre-processing operation, such as denoising,the low-dose image data before operation 604. As another example,operations 606 and/or 608 may be unnecessary and omitted. In someembodiments, process 600 may further include outputting the high-doseimage data generated in 606.

FIG. 7 is a flowchart illustrating an exemplary process 700 fordetermining a first neural network model according to some embodimentsof the present disclosure. Operation 604 as illustrated in FIG. 6 may beperformed according to process 700. In some embodiments, the firstneural network model may be configured to convert low-dose image data tohigh-dose image data. The first neural network model may be determinedby training a neural network model using multiple low-dose images andmultiple corresponding high-dose images. A low-dose image and acorresponding high-dose image may be reconstructed based on differentreconstruction techniques, respectively. In some embodiments, one ormore operations of process 700 illustrated in FIG. 7 for determining afirst neural network model may be implemented in the CT imaging system100 illustrated in FIG. 1. For example, the process 700 illustrated inFIG. 7 may be stored in the storage 130 in the form of instructions, andinvoked and/or executed by the processing engine 120 (e.g., theprocessor 210 of the computing device 200 as illustrated in FIG. 2, theGPU 330 or CPU 340 of the mobile device 300 as illustrated in FIG. 3).

In 702, high-dose projection data may be obtained. Operation 702 may beperformed by the image data simulation unit 540. In some embodiments,the high-dose projection data may include 2D projection data, 3Dprojection data, etc. In some embodiments, the high-dose projection datamay be obtained from a scanner (e.g., the scanner 110) generated byscanning a subject being examined. In some embodiments, the high-doseprojection data may be generated by a forward projection of an image. Insome embodiments, the high-dose projection data may be obtained from thestorage 130, the terminals 140, the storage module 450, and/or any otherexternal storage device.

In 704, low-dose projection data corresponding to the high-doseprojection data may be obtained. Operation 704 may be performed by theimage data simulation unit 540. As used herein, the correspondinglow-dose projection data and high-dose projection data may refer to therepresentation of a same subject or the same portion(s) of the subject(e.g., a patient, a tissue, etc.). In some embodiments, the high-doseprojection data may correspond to a first dose level, and the low-doseprojection may correspond to a second dose level. The first dose levelmay be greater than the second dose level. In some embodiments, thefirst dose level and the second dose level may vary according toclinical demands (e.g., a type of a tissue). For example, in a liverscan, the first dose level may be equal to or exceed 5 mSv, or 10 mSv,or 15 mSv, etc. The second dose level may be lower than 15 mSv, or 10mSv, or 5 mSv, etc. A ratio of the second dose level and the first doselevel may range from 5% to 40%, such as 10%, 15%, 20%, 25%, 30%, etc. Asanother example, in a chest scan, the first dose level may be equal toor exceed 2 mSv, or 7 mSv, etc. The second dose level may be lower than7 mSv, or 2 mSv, etc. In some embodiments, a ratio of the first doselevel and an estimated effective dose may be equal to or exceed 1%, or5%, or 10%, or 25%, or 50%, or 100%, or 150%, etc. A ratio of the seconddose level and the estimated effective dose may be equal to or below 1%,or 5%, or 10%, or 25%, etc. The estimated effective dose may be of adose level received in a region of interest for CT imaging in anintegrated scanning schedule. The dose level of the estimated effectivedose may range from, for example, 0.1 mSv to 1.5 mSv.

In some embodiments, the low-dose projection data may be obtained from ascanner (e.g., the scanner 110). In some embodiments, the low-doseprojection data may be obtained from the storage 130, the terminals 140,the storage module 450 and/or any other external storage device. In someembodiments, the low-dose projection data may be determined based on thehigh-dose projection data. For example, the low-dose projection data maybe determined by way of simulation based on the high-dose projectiondata.

It shall be noted that projection data may relate to a distribution ofradiation emitted from a scanner (e.g., the scanner 110) after theradiation passes through a subject being examined. The projection datamay include noise relating to the scanner (e.g., electronic noise of adetector in the scanner 110). The distribution of the radiation beamemitted from the scanner may relate to a scanning condition includingone or more scanning parameters including, such as, for example, a tubecurrent/voltage, an integration time of a detector, a focus size of atube, a response of a detector, a response of a tube, a width ofcollimation, etc. Different scanning conditions may be configured togenerate radiation beams of different dose levels. For example, thegreater the tube current/voltage is, the higher dose level the generatedradiation beams may be. In some embodiments, the low-dose projectiondata corresponding to the second dose level may be acquired based on thehigh-dose projection data corresponding to the first dose level asdescribed in connection with FIG. 8. In some embodiments, both thehigh-dose projection data and the corresponding low-dose projection datamay be obtained from a scanner (e.g., the scanner 110) generated byscanning a subject being examined.

In 706, a high-dose image may be generated based on the high-doseprojection data by a first reconstruction technique. Operation 706 maybe performed by the image reconstruction unit 520. In some embodiments,the first reconstruction technique may include an iterativereconstruction technique, an analytical reconstruction technique, or thelike, or a combination thereof. Exemplary iterative reconstructiontechniques may include an algebraic reconstruction technique (ART), asimultaneous iterative reconstruction technique (SIRT), a simultaneousalgebraic reconstruction technique (SART), an adaptive statisticaliterative reconstruction (ASIR) technique, a model based iterativereconstruction (MAIR) technique, a sinogram affirmed iterativereconstruction (SAFIR) technique, or the like, or a combination thereof.Exemplary analytical reconstruction techniques may include applying anFDK algorithm, a Katsevich algorithm, or the like, or a combinationthereof. In some embodiments, before a reconstruction process of thehigh-dose image, one or more reconstruction parameters may bedetermined. Exemplary reconstruction parameters may include a field ofview (FOV), a slice thickness, a reconstruction matrix, a slice gap, aconvolution kernel, or the like, or a combination thereof. For example,the high-dose image may be reconstructed by applying a larger slicethickness, a larger reconstruction matrix, a smaller FOV, etc., comparedto the reconstruction of a low-dose image.

In some embodiments, the high-dose image may exhibit a first imagequality. As used herein, the first image quality may be defined by afirst noise level of a high-dose or first image. In some embodiments,the first noise levels of high-dose images reconstructed based on thesame image data but different reconstruction techniques may different.For example, the first noise level of a high-dose image reconstructedusing an iterative reconstruction technique may be lower than that of ahigh-dose image reconstructed using an analytical reconstructiontechnique. In some embodiments, the first noise levels of high-doseimages reconstructed based on the same image data and the samereconstruction technique but different reconstruction parameters may bedifferent. For example, the first noise level of a high-dose imagereconstructed using a larger slice thickness, a larger reconstructionmatrix, a more smooth reconstruction kernel, and/or a smaller FOV, maybe lower than that of a high-dose image reconstructed based on a samereconstruction technique using a smaller slice thickness, a smallerreconstruction matrix, a sharper reconstruction kernel and/or a largerFOV.

In some embodiments, in the reconstruction process of the high-doseimage, a denosing technique or a filtering kernel function forperforming an image smoothing function may be used to decrease the firstnoise level of a high-dose image. Exemplary denoising techniques mayinclude an adaptive-filtering algorithm, a Karl-filtering algorithm, orthe like, or a combination thereof. Exemplary adaptive-filteringalgorithms may include a least mean squares (LMS) adaptive filteringalgorithm, a recursive least squares (RLS) adaptive filtering algorithm,a transform domain adaptive filtering algorithm, an affine projectionalgorithm, a conjugate gradient algorithm, an adaptive filteringalgorithm based on sub-band decomposition, an adaptive filteringalgorithm based on QR decomposition, etc. In some embodiments, thedenoising technique may include applying a denoising model. Exemplarydenoising models may include a spatial-domain filter model, atransform-domain filter model, a morphological noise filter model, orthe like, or a combination thereof. Exemplary spatial-domain filtermodels may include a field average filter model, a median filter model,a Gaussian filter model, or the like, or a combination thereof.Exemplary transform-domain filter models may include a Fourier transformmodel, a Walsh-Hadamard transform model, a cosine transform model, a K-Ltransform model, a wavelet transform model, or the like, or acombination thereof. In some embodiments, the denoising model mayinclude a partial differential model or a variational model, such as aPerona-Malik (P-M) model, a total variation (TV) model, or the like, ora combination thereof. Exemplary filtering kernel techniques forperforming an image smoothing function may include applying, forexample, a linear smoothing filter (e.g., a block filter, a mean filter,a Gaussian filter, etc.,), a nonlinear smoothing filter (e.g., a medianfilter, a sequential statistical filter, etc.), etc.

In 708, a low-dose image may be generated based on the low-doseprojection data by a second reconstruction technique. Operation 708 maybe performed by the image reconstruction unit 520. Exemplary secondreconstruction technique may include an iterative reconstructiontechnique, an analytical reconstruction technique, or the like, or acombination thereof, as described elsewhere in the disclosure. In someembodiments, the second reconstruction technique may be different fromor the same as the first reconstruction technique. For example, thesecond reconstruction technique may include an analytical reconstructiontechnique, and the first reconstruction technique may include aniterative reconstruction technique. As another example, the secondreconstruction technique and the first reconstruction technique mayinclude the same iterative reconstruction technique. In someembodiments, one or more reconstruction parameters may be determined forthe reconstruction of the low-dose image. Exemplary reconstructionparameters may include a field of view (FOV), a slice thickness, areconstruction matrix, a slice gap, or the like, or a combinationthereof. For example, the low-dose image may be reconstructed byapplying a smaller slice thickness, a smaller reconstruction matrix, alarger FOV, a sharper reconstruction kernel, etc., compared to areconstruction of a high-dose image.

In some embodiments, the low-dose image may exhibit a second imagequality. As used herein, the second image quality may be defined by asecond noise level of a low-dose or second image. The second noise levelof a low-dose image may be greater than the first noise level of acorresponding high-dose image. In some embodiments, the second noiselevels of low-dose images reconstructed based on the same image data butdifferent reconstruction techniques may different. For example, thesecond noise level of a low-dose image reconstructed using an analyticalreconstruction technique may be higher than that of a low-dose imagereconstructed using an iterative reconstruction technique. In someembodiments, the second noise levels of low-dose images reconstructedbased on the same image data and the same reconstruction technique butdifferent reconstruction parameters may be different For example, thesecond noise level of a low-dose image reconstructed using a smallerslice thickness, a smaller reconstruction matrix, a larger FOV, etc.,may be higher than that of a low-dose image reconstructed based on asame reconstruction technique using a larger slice thickness, a largerreconstruction matrix, a smaller FOV, etc.

In some embodiments, the second noise level of a low-dose image may beincreased by using a filtering kernel technique for performing an imagesharpening function. Exemplary filtering kernel techniques forperforming an image sharpening function may include applying, forexample, a linear sharpening filter (e.g., a Laplasse operator, a highfrequency lifting filter, etc.), a nonlinear sharpening filter (e.g., agradient based sharpening filter, a max-min sharpening transform, etc.),etc. In some embodiments, the second noise level of a low-dose image maybe decreased by using a filtering kernel technique for performing animage smoothing function and/or using a denoising technique as describedelsewhere in the disclosure.

In 710, a first neural network model may be determined based on thehigh-dose image and the low-dose image. In some embodiments, operation710 may be performed by the neural network training unit 560. In someembodiments, the first neural network model may be configured to improvethe quality of an image by way of, for example, reducing the noise levelof an image, increasing the contrast ratio of an image, or the like, ora combination thereof. In some embodiments, the effectiveness of thefirst neural network model for improving the quality of an image (e.g.,the function to reduce noise level) may relate to a difference betweenthe high-dose image and the low-dose image. As used herein, thedifference between the high-dose image and the low-dose image may referto the difference between the first noise level of the high-dose imageand the second noise level of the low-dose image. The larger thedifference between the first noise level of the high-dose image and thesecond noise level of the low-dose image is, the more efficient thefirst neural network model may be in improving the image quality byreducing the noise level of an image generated based on the first neuralnetwork model. As another example, the lower the first noise level ofthe high-dose image is, the more efficient the first neural network maybe in improving the image quality by reducing the noise level of alow-dose image generated based on the first neural network model. Thehigher the second noise level of the low-dose image is, the moreefficient the first neural network may be in improving the image qualityby reducing the noise level of a low-dose image generated based on thefirst neural network model. In some embodiments, the first neuralnetwork model may be determined by training a neural network model basedon a neural network training algorithm and high-dose images andcorresponding low-dose images. Exemplary neural network trainingalgorithms may include a gradient descent algorithm, a Newton'salgorithm, a Quasi-Newton algorithm, a Levenberg-Marquardt algorithm, aconjugate gradient algorithm, or the like, or a combination thereof.

In some embodiments, process 700 may be repeated with respect tomultiple training data including different groups of correspondinghigh-dose and low-dose projection data and images to improve or optimizethe first neural network model. In different rounds of process 700performed based on different pairs of high-dose and low-dose images, thehigh-dose images may be obtained based on the same or differentreconstruction techniques. In different rounds of process 700 performedbased on different pairs of high-dose and low-dose images, the low-doseimages may be obtained based on the same or different reconstructiontechniques.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,process 700 may include operations for pre-processing the high-doseprojection data and/or the low-dose projection data. As another example,operations 702 and 704 may be performed simultaneously or in a reverseorder than that illustrated in FIG. 7, and/or operations 706 and 708 maybe performed simultaneously or in a reverse order than that illustratedin FIG. 7. In some embodiments, process 700 may further includingstoring the high-dose image and the low-dose image in the storage 130,the terminals 140, the storage module 450, the storage unit 580, and/orother external storage devices. In some embodiments, the operations 706and 708 may be omitted. The first neural network model may be determinedbased on the high-dose projection data and the low-dose projection datadirectly. Therefore, the first neural network model may be configured toconvert an original set of projection data to a different set ofprojection data. The different set of projection data may exhibits alower noise level than the original set of projection data.

FIG. 8 is a flowchart illustrating an exemplary process 800 forgenerating simulated low-dose projection data according to someembodiments of the present disclosure. Operation 704 as illustrated inFIG. 7 may be performed according to process 800. In some embodiments,the generation of simulated projection data may relate to factorsincluding, for example, the scanning parameters of the scanner 110, anattenuation coefficient or absorption coefficient of a subject, ascattering coefficient of a subject, the noises corresponding to thescanner 110, or the like, or a combination thereof The scanningparameters may include, for example, a response of a detector, aresponse of a tube, a filtration of an anti-scattering grid, a value ofa tube current, a value of a tube voltage, a width of a collimation, atime of exposure (e.g., the duration of a scan), a size of focus, aflying focus mode, an integration time of the detector, etc. In someembodiments, one or more operations of process 800 illustrated in FIG. 8for determining a first neural network model may be implemented in theCT imaging system 100 illustrated in FIG. 1. For example, the process800 illustrated in FIG. 8 may be stored in the storage 130 in the formof instructions, and invoked and/or executed by the processing engine120 (e.g., the processor 210 of the computing device 200 as illustratedin FIG. 2, the CPU 340 of the mobile device 300 as illustrated in FIG.3).

In 802, a first distribution of first radiation may be determined. Thefirst radiation may be generated by a scan using a scanner (e.g., thescanner 110) in a first scanning condition. Operation 802 may beperformed by the acquisition module 410. The first distribution of thefirst radiation (e.g., radiation beams including X-ray photons) mayrefer to an incident intensity distribution of the first radiationbefore passing through a subject being examined. In some embodiments,the first distribution of the first radiation may be determined usingdetector units of the scanner. For example, the first distribution ofthe first radiation may be detected by the detector units of the scanner110 when the scanner 110 performs an air scan with no subject beingplaced between the X-ray generator and the detector units.

In some embodiments, the first distribution of the first radiation mayrelate to a first dose level of the first radiation. The first doselevel of the first radiation may be determined according to the firstscanning condition. The first scanning condition may be defined by thevalues of a plurality of first scanning parameters including, such as, avalue of a tube current, a value of a tube voltage, a width of acollimation, a time of exposure (e.g., the duration of a scan), afiltration of an anti-scattering grid, a response of a detector, aresponse of a tube (or the radiation source), a size of the focus of thetube, a flying focus mode, an integration time of the detector, etc. Thefirst dose level of the first radiation may be determined based on thevalue(s) of one or more of the plurality of the first scanningparameters. For example, the greater the tube current is, the higher thefirst dose level may be.

In 804, a second distribution of second radiation from the scanner maybe determined based on the first distribution of the first radiation.The second radiation may be virtual radiation that is simulatedaccording to a second scanning condition. Operation 802 may be performedby the image data simulation unit 540. Similarly, the seconddistribution of the second radiation (e.g., X-ray photons) may refer toan incident intensity distribution of the second radiation beforepassing through a subject being examined.

In some embodiments, the second distribution of the second radiation mayrelate to a second dose level of the second radiation. The second doselevel of the second radiation may be determined according to the secondscanning condition. The second scanning condition may be defined by thevalues of a plurality of second scanning parameters including, such as,for example, a value of a tube current, a value of a tube voltage, awidth of a collimation, a time of exposure (e.g., the duration of ascan), etc. The second dose level of the second radiation beam may bedetermined based on the values of the plurality of the second scanningparameters.

In some embodiments, the second distribution of the second radiationcorresponding to the second dose level (or in the second scanningcondition) may be determined based on the first distribution of thefirst radiation corresponding to the first dose level (or in the firstscanning condition). For example, a relationship between a distributionof radiation (e.g., a number distribution of particles/photons in aradiation beam) and a scanning condition (e.g., values of scanningparameters as described above) may be determined based on the firstscanning condition and the first distribution of the first radiation,and then the second distribution of the second radiation beam may bedetermined based on the relationship. For example, based on the firstdistribution of the first radiation, the second distribution of thesecond radiation may be determined according to a difference between thefirst scanning condition and the second scanning condition based on therelationship.

In 806, a third distribution of the second radiation in the secondscanning condition may be determined based on the second radiation andhigh-dose projection data. Operation 802 may be performed by the imagedata simulation unit 540. As used herein, the third distribution of thesecond radiation beam (e.g., X-ray photons) may refer to an exitingintensity distribution of the second radiation after the secondradiation passes through the subject being examined in the secondscanning condition. In some embodiments, the third distribution of thesecond radiation beam may be determined based on the second distributionof the second radiation and an attenuation distribution of the subject.In some embodiments, the attenuation distribution of the subject mayrelate to a distribution of an attenuation coefficient or an absorptioncoefficient of different portions of the subject. The distribution ofthe attenuation coefficient or the absorption coefficient may bedetermined by, reconstructing an attenuation map of the subject based onthe high-dose projection data. Then the third distribution of the secondradiation beam may be determined based on the second distribution of thesecond radiation and the attenuation distribution of the secondradiation corresponding to the subject.

In 808, a noise estimation relating to the scanner may be determined.Operation 802 may be performed by the image data simulation unit 540. Insome embodiments, the noise estimation relating to the scanner may bedetermined based on the detector units in the scanner. For example, thenoise estimation may be performed by detecting data with the detectorunits in the scanner when no radiation is emitted from the scanner. Thenoise may include the electronic noise occurred in the circuitsconnected to the detector units.

In 810, low-dose projection data may be determined based on the thirddistribution and the noise estimation. Operation 802 may be performed bythe image data simulation unit 540. In some embodiments, the low-doseprojection data may refer to the projection data corresponding to thesecond dose-level as described in 804 and 806. In some embodiments, aPoisson distribution relating to the second radiation may be determinedbased on the third distribution of the second radiation. The Poissondistribution may be determined to approximate (e.g., by way of curvefitting) the third distribution. Then the low-dose projection data maybe determined based on the Poisson distribution and the noiseestimation.

In some embodiments, the Poisson distribution and the noise estimationmay be mixed with a specific ratio to obtain the low-dose projectiondata. For example, the noise estimation may be represented by a firstmatrix including a plurality of first elements. The Poisson distributionrelating to the third distribution may be represented by a second matrixincluding a plurality of second elements. The plurality of firstelements and the plurality of second elements may be multiplied by afirst weight value and a second weight value, respectively. The low-doseprojection data may be determined by a weighted sum of the firstweighted elements and the second weighted elements. In some embodiments,the first weight value and the second value may be in a range from 0 to1.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,operations 802 and 808 may be performed simultaneously. As anotherexample, operation 808 may be performed before the operation 802

FIG. 9 is a flowchart illustrating an exemplary process 900 fordetermining a second neural network model according to some embodimentsof the present disclosure. Operation 608 as illustrated in FIG. 6 may beperformed according to process 900. In some embodiments, the secondneural network model may be configured to improve the quality (e.g., byimproving a contrast ratio) of an image. The second neural network modelmay be determined by training a neural network model with multipleimages with relatively high quality and multiple corresponding imageswith relatively low quality. The multiple images with relatively highquality and multiple corresponding images with relatively low qualitymay be reconstructed based on the same image data but differentreconstruction techniques, respectively. In some embodiments, one ormore operations of process 900 illustrated in FIG. 9 for determining afirst neural network model may be implemented in the CT imaging system100 illustrated in FIG. 1. For example, the process 900 illustrated inFIG. 9 may be stored in the storage 130 in the form of instructions, andinvoked and/or executed by the processing engine 120 (e.g., theprocessor 210 of the computing device 200 as illustrated in FIG. 2, theCPU 340 of the mobile device 300 as illustrated in FIG. 3).

In 902, projection data may be obtained. Operation 902 may be performedby the acquisition module 410. In some embodiments, the projection datamay include high-dose projection data as described in connection withoperation 702.

In 904, a first image may be generated based on the projection data by athird reconstruction technique. Operation 904 may be performed by theimage reconstruction unit 520. The third reconstruction techniqueinclude an iterative reconstruction technique, an analyticalreconstruction technique, or the like, or a combination thereof, asdescribed elsewhere in the disclosure. In some embodiments, before areconstruction process of the first image, one or more reconstructionparameters as described elsewhere in the disclosure may be determined.See, for example, operations 706 and/or 708 of FIG. 7 and relevantdescription thereof.

In 906, a second image may be generated based on the projection data bya fourth reconstruction technique. Operation 906 may be performed by theimage reconstruction unit 520. The second image may have a highercontrast ratio than the first image.

In some embodiments, the fourth reconstruction technique may bedifferent from the third reconstruction technique. For example, thethird reconstruction technique may include an analytical reconstructiontechnique, and the fourth reconstruction technique may include aniterative reconstruction technique. In some embodiments, thereconstruction parameters used in the third reconstruction technique maybe different from the reconstruction parameters used in the fourthreconstruction technique. For example, the third reconstructiontechnique may use a larger slice thickness, a smaller reconstructionmatrix, and/or a larger FOV, compared to the fourth reconstructiontechnique. In some embodiments, the third reconstruction technique andthe fourth reconstruction technique may be of the same type but based ondifferent reconstruction parameters. For instance, the thirdreconstruction technique and the fourth reconstruction technique may beof an iterative reconstruction technique but based on differentreconstruction parameters. In some embodiments, the third reconstructiontechnique and the fourth reconstruction technique may be of differenttypes based on the same or different reconstruction parameters.

The denoising process or filtering kernel for performing an imagesmoothing function may decrease the contrast ratio of an image. Thefiltering kernel for performing an image sharpening function mayincrease the contrast ratio of an image. In some embodiments, adenoising process or a filtering kernel for performing an imagesmoothing function as described elsewhere in the disclosure may be usedin the third reconstruction technique. See, for example, operations 706and/or 708 of FIG. 7 and relevant description thereof. Additionally oralternatively, a filtering kernel for performing an image sharpeningfunction as described elsewhere in the disclosure may be used in thefourth reconstruction technique. See, for example, operations 706 and/or708 of FIG. 7 and relevant description thereof. Therefore, the secondimage may exhibit a higher contrast than that of the first image.

In 908, a second neural network model may be determined based on thefirst image and the second image. In some embodiments, operation 908 maybe performed by the neural network model training unit 560. In someembodiments, the second neural network model may be configured toimprove the quality of an image by way of, for example, increasing thecontrast ratio of the image. The effectiveness of the second neuralnetwork for improving the contrast ratio of the image may relate to adifference between the first image and the second image. As used herein,the difference between the first image and the second image may refer tothe difference between the first contrast ratio of the first image andthe second contrast ratio of the second image. The larger the differencebetween the first contrast ratio of the first image and the secondcontrast ratio of the second image is, the more efficient the secondneural network may be in improving the image quality by increasing thecontrast ratio of an image generated based on the second neural networkmodel. As another example, the lower the first contrast ratio of thefirst image is, the more efficient the second neural network may be inimproving the image quality by increasing the contrast ratio of an imagegenerated based on the second neural network model. The higher thesecond contrast ratio of the second image is, the more efficient thesecond neural network may be in improving the image quality byincreasing the contrast ratio of an image generated based on the secondneural network model. In some embodiments, the second neural networkmodel may be determined by training a neural network model based on aneural network training algorithm and multiple first images andcorresponding second images. Exemplary neural network training algorithmmay include a gradient descent algorithm, a Newton's algorithm, aQuasi-Newton algorithm, a Levenberg-Marquardt algorithm, a conjugategradient algorithm, or the like, or a combination thereof.

In some embodiments, process 900 may be repeated with respect tomultiple training data including different projection data to improve oroptimize the second neural network model. In different rounds of process900 performed based on different pairs of high-dose and low-dose (orfirst and second) images, the high-dose (or first) images may beobtained based on the same or different reconstruction techniques. Indifferent rounds of process 900 performed based on different pairs ofhigh-dose and low-dose (or first and second) images, the low-dose (orsecond) images may be obtained based on the same or differentreconstruction techniques.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,process 900 may include pre-processing the projection data. As anotherexample, operations 904 and 906 may be performed simultaneously or in areverse order than that illustrated in FIG. 9.

FIG. 10 is a flowchart illustrating an exemplary process 1000 fortraining a neural network model according to some embodiments of thepresent disclosure. Operation 710 as illustrated in FIG. 7 and/oroperation 908 as illustrated in FIG. 9 may be performed according toprocess 1000. In some embodiments, one or more operations of process1000 illustrated in FIG. 10 for training a neural network model may beimplemented in the CT imaging system 100 illustrated in FIG. 1. Forexample, the process 1000 illustrated in FIG. 10 may be stored in thestorage 130 in the form of instructions, and invoked and/or executed bythe processing engine 120 (e.g., the processor 210 of the computingdevice 200 as illustrated in FIG. 2, the CPU 340 of the mobile device300 as illustrated in FIG. 3).

In 1002, a pair of images including a third image and a fourth image maybe obtained. Operation 1002 may be performed by the acquisition module410. As used herein, the third image and the fourth image may refer totwo images representing a same subject or a same region of the subjectas being examined (e.g., a patient, a tissue, etc.). In someembodiments, the third image and the fourth image may correspond to alow-dose image and a high-dose image, respectively, as described in FIG.7. In some embodiments, the third image and the fourth image maycorrespond to a first image and a second image, respectively, asdescribed in FIG. 9.

In 1004, a neural network model including one or more parameters may beinitialized. Operation 1004 may be performed by the initialization block562. In some embodiments, the initialization of the neural network modelmay include constructing the neural network model based on aconvolutional neural network (CNN), a recurrent neural network (RNN), along short-term memory (LSTM), a generative adversarial network (GAN),or the like, or a combination thereof, as exemplified in FIG. 11 and thedescription thereof. In some embodiments, the neural network model mayinclude multiple layers, for example, an input layer, multiple hiddenlayers, and an output layer. The multiple hidden layers may include oneor more convolutional layers, one or more batch normalization layers,one or more activation layers, a fully connected layer, a cost functionlayer, etc. Each of the multiple layers may include a plurality ofnodes.

In some embodiments, the parameters of the neural network model mayinclude the size of a convolutional kernel, the number of layers, thenumber of nodes in each layer, a connected weight between two connectednodes, a bias vector relating to a node, etc. The connected weightbetween two connected nodes may be configured to represent a proportionof an output value of a node to be as an input value of anotherconnected node. In some embodiments, the connected weights of the neuralnetwork model may be initialized to be random values in a range, e.g.,the range from −1 to 1. In some embodiments, all the connected weightsof the neural network model may have a same value in the range from −1to 1, for example, 0. The bias vector relating to a node may beconfigured to control an output value of the node deviating from anorigin. In some embodiments, the bias vector of nodes in the neuralnetwork model may be initialized to be random values in a range from 0to 1. In some embodiments, the parameters of the neural network modelmay be initialized based on a Gaussian random algorithm, a xavieralgorithm, etc.

In 1006, a first region may be extracted from the third image. Operation1006 may be performed by the extraction block 564. In some embodiments,the first region may be extracted according to, for example, a size ofthe first region, a position of the first region, etc. For example, afirst position may be determined in the first image, then the firstregion with a specific size may be extracted at the first position fromthe first image. In some embodiments, the first region may be extractedbased on a random sampling algorithm. Exemplary random samplingalgorithms may include an acceptance-rejection sampling algorithm, animportance sampling algorithm, a Metropolis-Hasting algorithm, a Gibbssampling algorithm, etc. In some embodiments, the first region may beextracted based on an instruction provided by a user via the terminals140. For example, the user may determine a coordinate of the firstposition in the first image and a specific size of the first region, andthen the extraction block 564 may extract the first region based on thefirst position and the specific size of the first region.

In 1008, a second region corresponding to the first region may beextracted from the fourth image. Operation 1008 may be performed by theextraction block 564. As used herein, the second region corresponding tothe first region may refer to that the first region and the secondregion may be of the same size and at the same position in the thirdimage and the fourth image, respectively. In some embodiments, thesecond region may be extracted based on the first region. For example,the third image may be divided into multiple first image blocksaccording to a division rule, for example, an even division. Themultiple first image blocks may be numbered according to a numberingrule, for example a position of each of the multiple first image blocks.A first block with a specific number may be extracted from the multiplefirst image blocks and designated as the first region. The fourth imagemay be divide into multiple second image blocks with the same divisionrule as the first image. Each of the multiple second image blocks may benumbered with the same numbering rule as the first image. A second blockwith the same number as the extracted first region may be extracted fromthe multiple second image blocks and designated as the second region. Asanother example, the position of the first/second region with respect tothe third/fourth image may relate to the location of the first/secondregion stored in a storage, e.g., the storage unit 580. The secondregion with respect to the fourth image may be determined according tothe location of the first region with respect to the third image in thestorage.

In 1010, a value of a cost function (also referred to as a lossfunction) may be determined. Operation 1010 may be performed by thecalculation block 566. The cost function may be configured to assess adifference between a testing value (e.g., the first region of the thirdimage) of the neural network and a desired value (e.g., the secondregion of the fourth image). In some embodiments, the first region ofthe third image may be inputted to the neural network model via an inputlayer (e.g., the input layer 1120 as illustrated in FIG. 11). The firstregion of the third image may be transferred from a first hidden layerof the neural network model (e.g., the conventional layers 1140-1 asillustrated in FIG. 11) to the last hidden layer of the neural networkmodel. The first region of the third image may be processed in each ofthe multiple hidden layers. For example, the inputted first region ofthe third image may be processed by one or more conventional layer(e.g., the conventional layers 1140-1 as illustrated in FIG. 11). Theone or more conventional layers may be configured to perform an imagetransformation operation, an image enhancement operation, an imagedenoising operation, or any other operations on the first region of thethird image based on the parameters relating to nodes in the one or moreconventional layers. The processed first region of the third imageprocessed by the hidden layers before the cost function layer may beinputted to the cost function layer. The value of the cost functionlayer may be determined based on the processed first region of the thirdimage processed by the layers before the cost function layers and thesecond region of the fourth image.

In 1012, a determination may be made as to whether a first condition issatisfied. Operation 1012 may be performed by the judgment block 568. Ifthe first condition is satisfied, process 1012 may proceed to operation1016. If the first condition is not satisfied, process 1000 may proceedto 1014. The first condition may provide an indication whether theneural network model is sufficiently trained * * * . In someembodiments, the first condition may relate to a value of the costfunction. For example, the first condition may be satisfied if the valueof the cost function is minimal or smaller than a threshold (e.g., aconstant). As another example, the first condition may be satisfied ifthe value of the cost function converges. In some embodiments,convergence may be deemed to have occurred if the variation of thevalues of the cost function in two or more consecutive iterations isequal to or smaller than a threshold (e.g., a constant). In someembodiments, convergence may be deemed to have occurred if a differencebetween the value of the cost function and a target value is equal to orsmaller than a threshold (e.g., a constant). In some embodiments, thefirst condition may be satisfied when a specified number of iterationsrelating to the first region of the third image and the second region ofthe fourth image are performed in the training process.

In 1014, the one or more parameters of the neural network model may beupdated. Operation 1014 may be performed by the initialization block562. In some embodiments, the parameter value of at least some nodes maybe adjusted until the value of the cost function relating to the firstregion of the third image satisfy the first condition. In someembodiments, the parameters of the neural network model may be adjustedbased on a back-propagation (BP) algorithm. Exemplary BP algorithms mayinclude a stochastic gradient descent algorithm, an Adam algorithm, anAdagrad algorithm, an Adadelta algorithm, an RMSprop algorithm, or thelike, or a combination thereof.

In 1016, a determination may be made as to whether the second conditionis satisfied. Operation 1016 may be performed by the judgment block 568.If the second condition is satisfied, process 1000 may proceed to 1018.If the second condition is not satisfied, process 1000 may return to1004, another first region may be extracted from the third image. Insome embodiments, the second condition may be satisfied if a specifiednumber of the first regions and the second regions are processedassociated with the neural network model.

In 1018, an updated neural network model may be determined. Operation1018 may be performed by the initialization block 562. In someembodiments, the updated neural network model may be determined based onthe updated parameters.

In some embodiments, process 1000 may be repeated with respect tomultiple training data including different pairs of third and fourthimages to improve or optimize the neural network model. In differentrounds of process 1000 performed based on different pairs of third andfourth images, the third images may be obtained based on the same ordifferent reconstruction techniques. In different rounds of process 1000performed based on different pairs of third and fourth images, thefourth images may be obtained based on the same or differentreconstruction techniques. Except for the first round of process 1000,in a subsequent round of process 1000, the initialization of the neuralnetwork model in 1004 may be performed based on the updated parametersof the neural network model obtained in a preceding round.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example,process 1000 may be performed repeatedly based on multiple third imagesand fourth images to obtain the first neural network model and/or thesecond neural network model. The training process may be performed untila termination condition is satisfied. An exemplary termination conditionis that a specific number of pairs of third images and fourth imageshave been analyzed.

FIG. 11 is a schematic diagram illustrating an exemplary convolutionalneural network (CNN) model according to some embodiments of the presentdisclosure.

The CNN model may include an input layer 1120, hidden layers 1140, andan output layer 1160. The multiple hidden layers 1140 may include one ormore convolutional layers, one or more Rectified Linear Units layers(ReLU layers), one or more pooling layers, one or more fully connectedlayers, or the like, or a combination thereof.

For illustration purposes, exemplary hidden layers 1140 of the CNNmodel, including a convolutional layer 1140-1, a pooling layer 1140-2,and a fully connected layer 1140-N, are illustrated. As described inconnection with process 708, the neural network training unit 560 mayacquire a low-dose image as an input of the CNN model. The low-doseimage may be expressed as a two-dimensional (2D) or three-dimensional(3D) matrix including a plurality of elements (e.g., pixels or voxels).Each of the plurality of elements in the matrix may have a value (alsoreferred to as pixel/voxel value) representing a characteristic of theelement.

The convolutional layer 1140-1 may include a plurality of kernels (e.g.,A, B, C, and D). The plurality of kernels may be used to extractfeatures of the low-dose image. In some embodiments, each of theplurality of kernels may filter a portion (e.g., a region) of thelow-dose image to produce a specific feature corresponding to theportion of the low-dose image. The feature may include a low-levelfeature (e.g., an edge feature, a texture feature), a high-levelfeature, or a complicated feature that is calculated based on thekernel(s).

The pooling layer 1140-2 may take the output of the convolutional layer1140-1 as an input. The pooling layer 1140-2 may include a plurality ofpooling nodes (e.g., E, F, G, and H). The plurality of pooling nodes maybe used to sample the output of the convolutional layer 1140-1, and thusmay reduce the computational load of data processing and increase thespeed of data processing of the CT imaging system 100. In someembodiments, the neural network training unit 560 may reduce the volumeof the matrix corresponding to the low-dose image in the pooling layer1140-2.

The fully connected layer 1140-N may include a plurality of neurons(e.g., O, P, M, and N). The plurality of neurons may be connected to aplurality of nodes from the previous layer, such as a pooling layer. Inthe fully connected layer 1140-N, the neural network training unit 560may determine a plurality of vectors corresponding to the plurality ofneurons based on the features of the low-dose image and further weighthe plurality of vectors with a plurality of weighting coefficients.

In the output layer 1160, the neural network training unit 560 maydetermine an output, such as a high-dose image, based on the pluralityof vectors and weighting coefficients obtained in the fully connectedlayer 708.

It shall be noted that the CNN model may be modified when applied indifferent conditions. For example, in a training process, a lossfunction (also referred to as cost function in the disclosure) layer maybe added to specify the deviation between a predicted output (e.g., apredicted high-dose image) and a true label (e.g., a reference high-doseimage corresponding to the low-dose image).

In some embodiments, the neural network training unit 560 may get accessto multiple processing units, such as GPUs, in the CT imaging system100. The multiple processing units may perform parallel processing insome layers of the CNN model. The parallel processing may be performedin such a manner that the calculations of different nodes in a layer ofthe CNN model may be assigned to two or more processing units. Forexample, one GPU may run the calculations corresponding to kernels A andB, and the other GPU(s) may run the calculations corresponding tokernels C and D in the convolutional layer 1140-1. Similarly, thecalculations corresponding to different nodes in other type of layers inthe CNN model may be performed in parallel by the multiple GPUs.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, for example, aninstallation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and describe.

1. A method implemented on a computing device having at least one processor and at least one computer-readable storage medium, the method comprising: obtaining a first set of projection data with respect to a first dose level acquired by a scanner; reconstructing, based on the first set of projection data, a first image; determining, based on the first set of projection data, a second set of projection data, the second set of projection data relating to a second dose level that is lower than the first dose level; reconstructing, based on the second set of projection data, a second image; and training a first neural network model based on the first image and the second image, the trained first neural network model being configured to convert a third image to a fourth image, the fourth image exhibiting a lower noise level than the third image.
 2. The method of claim 1, wherein the first neural network model is structured based on at least one of a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), a Long Short-Term Memory (LSTM), or a Generative Adversarial Network (GAN).
 3. The method of claim 1, wherein the first image is reconstructed based on an iterative reconstruction algorithm with first reconstruction parameters, the second image is reconstructed based on an analytical reconstruction algorithm or an iterative reconstruction algorithm with second reconstruction parameters, and the second reconstruction parameters are at least partially different from the first parameters.
 4. (canceled)
 5. The method of claim 1, wherein the first image is reconstructed by applying at least one of a larger slice thickness, a larger reconstruction matrix, or a smaller FOV, compared to the reconstruction of the second image.
 6. The method of claim 1, wherein the second set of projection data is determined based on at least one of a scanning parameter of the scanner that acquires the first projection data, an attenuation coefficient relating to a subject, and noises corresponding to the scanner, a response of a tube, a response of a detector of the scanner, a size of a focus of the scanner, a flying focus of the scanner, an integration time of the detector of the scanner, or a scattering coefficient of the subject.
 7. The method of claim 1, wherein the determining the second set of projection data comprises: determining a first distribution of radiation with respect to the second dose level before the radiation passing through a subject; determining, based on the first distribution of the radiation and the first set of projection data, a second distribution of the radiation after the radiation passing through the subject; determining a noise estimation of the scanner; and determining, based on the second distribution of the radiation and the noise estimation, the second set of projection data.
 8. The method of claim 7, wherein the determining the noise estimation comprises: detecting a response of detectors in the scanner when no radiation is emitted from the scanner.
 9. The method of claim 1, wherein the training the first neural network model based on the first image and the second image comprises: extracting, from the first image, a first region; extracting, from the second image, a second region corresponding to the first region in the first image, the first region in the first image having a same size as the second region; and training the first neural network model based on the first region in the first image and the second region in the second image.
 10. The method of claim 9, wherein the training the first neural network model based on the first region in the first image and the second region in the second image comprises: initializing parameter values of the first neural network model; iteratively determining, at least based on the first region in the first image and the second region in the second image, a value of a cost function relating to the parameter values of the first neural network model in each iteration, including updating at least some of the parameter values of the first neural network model after each iteration based on an updated value of the cost function obtained in a most recent iteration; and determining the trained first neural network model until a condition is satisfied.
 11. The method of claim 10, wherein the condition includes that a variation of the values of the cost function among a plurality of iterations is below a threshold, or a threshold number of the iterations have been performed.
 12. The method of claim 1, furthering comprising: training a second neural network model, wherein the second neural network model is trained based on a sixth image and a seventh image, wherein the sixth image is reconstructed based on a third set of projection data, wherein the seventh image is reconstructed based on the third set of projection data, wherein an image quality of the seventh image is greater than that of the sixth image, the image quality relating to at least one of a contrast ratio and a spatial resolution.
 13. The method of claim 12, wherein the third set of projection data includes the first set of projection data.
 14. The method of claim 1, wherein a dimension of the first image or the first neural network model is no less than two.
 15. The method of claim 1, wherein the first dose level is 5 mSv or above, or 15 mSv or above.
 16. (canceled)
 17. The method of claim 1, wherein the second dose level is no more than 10% of the first dose level, or no more than 40% of the first dose level.
 18. (canceled)
 19. A method implemented on a computing device having at least one processor and at least one computer-readable storage medium, the method comprising: obtaining a first set of projection data with respect to a first dose level acquired by a scanner; determining, based on a first neural network model and the first set of projection data, a second set of projection data with respect to a second dose level that is higher than the first dose level; generating, based on the second set of projection data, a first image; and generating, based on a second neural network model and the first image, a second image.
 20. The method of claim 19, wherein the first neural network is generated by: obtaining a third set of projection data with respect to a third dose level; simulating, based on the third set of projection data, a fourth set of projection data, the fourth set of projection data relating to a fourth dose level that is lower than the third dose level; and training the first neural network model based on the third set of projection data and the fourth set of projection data.
 21. The method of claim 20, wherein the simulating the fourth set of projection data comprises: determining a first distribution of a radiation with respect to the fourth dose level before the radiation passing through a subject; determining, based on the first distribution of the radiation and the third set of projection data, a second distribution of the radiation after the radiation passing through the subject; determining a noise estimation of the scanner that acquires the first set of projection data; and simulating, based on the second distribution of the radiation and the noise estimation, the fourth set of projection data.
 22. The method of claim 19, wherein the second neural network is generated by obtaining a third image, the third image being reconstructed based on a fifth set of projection data, obtaining a fourth image, the fourth image being reconstructed based on the fifth set of projection data, and training the second neural network model based on the third image and the fourth image, wherein an image quality of the fourth image is greater than that of the third image, the image quality relating to at least one of a contrast ratio and a spatial resolution.
 23. A system, comprising: a computer-readable storage medium storing executable instructions, and at least one processor in communication with the computer-readable storage medium, when executing the executable instructions, causing the system to implement a method comprising: obtaining a first set of projection data with respect to a first dose level; reconstructing, based on the first set of projection data, a first image; determining, based on the first set of projection data, a second set of projection data, the second set of projection data relating to a second dose level that is lower than the first dose level; reconstructing, based on the second set of projection data, a second image; and training a first neural network model based on the first image and the second image, the trained first neural network model being configured to convert a third image to a fourth image, the fourth image exhibiting a lower noise level than the third image. 24-30. (canceled) 